Convergent network effects along the axis of gene expression during prostate cancer progression

• Main Authors: Konstantina Charmpi, Tiannan Guo, Qing Zhong, Ulrich Wagner, Rui Sun, Nora C. Toussaint, Christine E. Fritz, Chunhui Yuan, Hao Chen, Niels J. Rupp, Ailsa Christiansen, Dorothea Rutishauser, Jan H. Rüschoff, Christian Fankhauser, Karim Saba, Cedric Poyet, Thomas Hermanns, Kathrin Oehl, Ariane L. Moore, Christian Beisel, Laurence Calzone, Loredana Martignetti, Qiushi Zhang, Yi Zhu, María Rodríguez Martínez, Matteo Manica, Michael C. Haffner, Ruedi Aebersold, Peter J. Wild, Andreas Beyer
• Format: Article
• Language: English
• Published: BMC 2020-12-01
• Series: Genome Biology
• Subjects: Molecular aberrations; Network effects; Prostate cancer; Proteogenomic analysis; Tumor heterogeneity
• Online Access: https://doi.org/10.1186/s13059-020-02188-9

Abstract Background Tumor-specific genomic aberrations are routinely determined by high-throughput genomic measurements. It remains unclear how complex genome alterations affect molecular networks through changing protein levels and consequently biochemical states of tumor tissues. Results Here, we investigate the propagation of genomic effects along the axis of gene expression during prostate cancer progression. We quantify genomic, transcriptomic, and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis reveals the convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devise a network-based approach that integrates perturbations across different molecular layers, which identifies a sub-network consisting of nine genes whose joint activity positively correlates with increasingly aggressive tumor phenotypes and is predictive of recurrence-free survival. Further, our data reveal a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. Conclusions This study uncovers molecular networks with considerable convergent alterations across tumor sites and patients. It also exposes a diversity of network effects: we could not identify a single sub-network that is perturbed in all high-grade tumor regions.